Description: 落煤残存瓦斯量的确定是采掘工作面瓦斯涌出量预测的重要环节,它直接影响着采掘工作面瓦斯涌出量预测的精度,并与煤的变质程度、落煤粒度、原始瓦斯含量、暴露时间等影响因素呈非线性关系。人工神经网络具有表示任意非线性关系和学习的能力,是解决复杂非线性、不确定性和时变性问题的新思想和新方法。基于此,作者提出自适应神经网络的落煤残存瓦斯量预测模型,并结合不同矿井落煤残存瓦斯量的实际测定结果进行验证研究。结果表明,自适应调整权值的变步长BP神经网络模型预测精度高,收敛速度快 该预测模型的应用可为采掘工作面瓦斯涌出量的动态预测提供可靠的基础数据,为采掘工作面落煤残存瓦斯量的确定提出了一种全新的方法和思路。-charged residual coal gas is to determine the volume of mining gas emission rate forecast an important link, which directly affect mining gas emission rate forecast accuracy, and with coal metamorphism, loading coal particle size, the original gas content, exposure time and other factors nonlinear relationship. Artificial neural networks have expressed arbitrary nonlinear relationships and the ability to solve complex nonlinear, time-varying uncertainty and the new ideas and new approaches. Based on this, the author of adaptive neural network loading coal residual gas production forecast model, and a combination of different loading coal mine gas remnants of the actual test results of research. Results show that the adaptive value of the right to change step BP neural network model predict Platform: |
Size: 60416 |
Author:王静 |
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Description: 这个应该是经典编程里面的经典算法实现的八皇后问题．也通过了测试．完全达到设计要求．-This should be a classic of the classic programming algorithm inside the eight Queen s problems. Also passed the test. Fully meet the design requirements. Platform: |
Size: 1024 |
Author:陈天 |
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Description: 实现的是最小费用最大流问题，在残量网络中找s-t最短路，运用bellman-ford算法。-To achieve the minimum cost maximum flow problem, in the residual network, find the shortest st, the use of bellman-ford algorithm. Platform: |
Size: 1024 |
Author:张佳静 |
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Description: 自己编写的关于无线脚本的trace文件分析awk文件。
在无线网络层仿真中，使用NS2软件的必修课就是使用AWK文件分析TRACE文件。
我所写的网络层方面的仿真大多都用这几个文件作分析--时延、吞吐量、抖动、剩余能量、丢包率。希望对你有用。-Awk file decoded by myself which can analysis trace file in wireless scenery.
In wireless simulation on network layer, using awk file to analysis trace file is inevitable.
i always use these awk file to analysis delay, throughput,jitter, residual energy and loss rate. I hope it can help you. Platform: |
Size: 2048 |
Author:许瑞琛 |
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Description: 本文提出了一种节点密度和剩余能量传感器网络加权分群算法，该分群算法优化了群首
选择，均衡了网络节点的能量消耗，延长了网络的生命周期-This paper proposes a residual energy of sensor nodes in the network density and weighted clustering algorithm, the clustering algorithm to optimize the group' s first choice, balancing the energy consumption of network nodes to extend the network life cycle Platform: |
Size: 284672 |
Author:吴田 |
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Description: 网络流终极解包裹程序，自动绕过包裹相位图的残差点，得到好的解包裹结果。-The ultimate solution of network flow wrapping other programs to automatically bypass the wrapped phase map of the residual was almost good unwrapping results. Platform: |
Size: 992256 |
Author:shingoxy |
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Description: A mobile ad hoc network is collection of mobile nodes.
The mobile nodes can move freely from one place to another. In
this paper, we propose a new routing algorithm for the mobile ad
hoc networks to route the data packets to the destination based on
the residual battery capacity and signal strength. so here we describe aodv routing in wireless ad-hoc network. Platform: |
Size: 22590464 |
Author:anurag |
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Description: Edmonds算法，寻找网络最大流
算法流程:
在残余网络中, 通过广搜找到一条增广路
找出增广路上的最小权值的边
更新残余网络
直到找不到增广路为止
// 统计当前网络中汇点的流-Edmonds algorithm to find the maximum network flow algorithm process: in the residual network, through extensive search to find an augmenting path to find the road augmentation minimum weight edge update the residual network until no augmenting path until the// current network statistics Meeting Point in the stream Platform: |
Size: 1024 |
Author:qhs |
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Description: The energy model represents the energy level of nodes in the network. The energy model defined in a node has an initial value that is the level of energy the node has at the beginning of the simulation. This energy is termed as initial Energy_. In simulation, thevariable “energy” represents the energy level in a node at any specified time.The value of initial Energy_ is passed as an input argument. A node looses a particular amount of energy for every packet transmitted and every packet received. As a result, the value of initial Energy_ in a node gets decreased.The current value of energy in a node after receiving or transmitting routing packets is the residual energy. Data Transmission is established between nodes using UDP agent and CBR traffic. Residual energy of the node is uated by accessing inbuilt variable “energy” in find Energy procedure at different times.-The energy model represents the energy level of nodes in the network. The energy model defined in a node has an initial value that is the level of energy the node has at the beginning of the simulation. This energy is termed as initial Energy_. In simulation, thevariable “energy” represents the energy level in a node at any specified time.The value of initial Energy_ is passed as an input argument. A node looses a particular amount of energy for every packet transmitted and every packet received. As a result, the value of initial Energy_ in a node gets decreased.The current value of energy in a node after receiving or transmitting routing packets is the residual energy. Data Transmission is established between nodes using UDP agent and CBR traffic. Residual energy of the node is uated by accessing inbuilt variable “energy” in find Energy procedure at different times. Platform: |
Size: 65536 |
Author:saadawais |
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Description: 深度残差网络的介绍与源代码，适合深度学习爱好者学习。这是何凯明大牛的又一部大作。-The depth of the residual network is introduced with the source code , suitable for deep learning lovers to study . This is another masterpiece He Kaiming Daniel . Platform: |
Size: 17408 |
Author:黄杰 |
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Description: Energy is one of the most important items to determine the network lifetime due to low power energy nodes included in the network. Generally, data aggregation tree concept is used to find an energy efficient solution. However, even the best aggregation tree does not share the load of data packets to the transmitting nodes fairly while it is consuming the lowest possible energy of the network. Therefore, after some rounds, this problem causes to consume the whole energy of some heavily loaded nodes and hence results in with the death of the network. In this paper, by using the Genetic Algorithm (GA), we investigate the energy efficient data collecting spanning trees to find a suitable route which balances the data load throughout the network and thus balances the residual energy in the network in addition to consuming totally low power of the network. Using an algorithm which is able to balance the residual energy among the nodes can help the network to withstand more and consequently extend its own lifetime. In this work, we calculate all possible routes represented by the aggregation trees through the genetic algorithm. GA finds the optimum tree which is able to balance the data load and the energy in the network. Simulation results show that this balancing operation practically increases the network lifetime. Platform: |
Size: 342534 |
Author:atrakpc@yahoo.com |
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Description: 残差网络模型,通过将原始输入直接通过桥梁输送到后面的几层，减少梯度弥散(The residual network model reduces the gradient dispersion by transmitting the original input directly through the bridge to the latter layers) Platform: |
Size: 346112 |
Author:zzqzzq123
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Description: 在使用深度神经网络时我们一般推荐使用大牛的组推出的和成功的网络。如最近的google团队推出的BN-inception网络和inception-v3以及微软最新的深度残差网络ResNET。(In the use of deep neural network we generally recommend the use of cattle group launched and successful network. Such as the recent google team launched BN-inception network and inception-v3 and Microsoft's latest depth of residual network ResNET.) Platform: |
Size: 20480 |
Author:刘浩迪
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Description: 使用 TensorFlow 实现 resNet, 也就是残差网络，为官方demo， 分别用 cifar 数据集和 ImageNet 数据集进行测试。(Using TensorFlow to achieve resNet, that is, the residual network, for official demo, respectively, using cifar data sets and ImageNet data sets for testing.) Platform: |
Size: 118784 |
Author:大幕
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Description: facebook公司开发的基于torch的残差网络深度学习模型(Torch based depth learning model based on residual network developed by Facebook company) Platform: |
Size: 51200 |
Author:妮可内姆 |
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Description: 结合VGG和残差网络实现工业零件的缺陷检测，基于keras和tensorflow可以直接运行使用(The defect detection of industrial parts is realized by combining VGg and residual network. Based on keras and tensorflow, it can be used directly) Platform: |
Size: 13312 |
Author:xiaozhanggao |
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Search - residual network